Prior Knowledge and Learning in 3D Object Recognition

  • Markus Gschwind
  • Hans Brettel
  • Ingo Rentschler


Biological 3D object recognition is restricted to the sensing of 2D projections, or images, and is further constrained by the lack of transparency. The most common assumption then is that image data are referenced to mental object representations. Such representations, or object models, must be contrasted with object recognition in so far as the latter involves the understanding of image data. This distinction is central to recognition-by-components (RBC; Biederman 1987), a theory of human image understanding based on the assumption that input images are parsed into regions that display nonaccidental properties of edges. These properties provide critical constraints on the identity of 3D primitives (“geons”) the images come from, e.g., cylinders, blocks, wedges, and cones, and are (relatively) invariant with viewpoint and image degradation.


Object Recognition Mental Rotation Object Representation Category Learning Vision Priming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2007

Authors and Affiliations

  • Markus Gschwind
    • 1
  • Hans Brettel
    • 2
  • Ingo Rentschler
    • 1
  1. 1.Institute of Medical PsychologyUniversity of MunichMünchenGermany
  2. 2.École Nationale Supérieure des TélécommunicationsCNRS UMR 5141ParisFrance

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